Learning environmental field exploration with computationally constrained underwater robots : Gaussian processes meet stochastic optimal control

Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.

Schlagworte

autonomous exploration

environmental field monitoring

Gaussian processes

Gaussian Markov random fields

Kalman filtering

stochastic optimal control

DDC Class

510: Mathematik

600: Technik

620: Ingenieurwissenschaften

Projekt(e)

Open Access Publizieren 2018 - 2019 / TU Hamburg

Zustandsschätzung von Strömungsfeldern und Quellfindung mittels dynamisch positionierter Unterwasser-Sensorknoten.

Dezentrale kooperative Exploration von nichtstationären räumlich und zeitlich verteilten Feldern mit autonomen Unterwasserfahrzeugen

More Funding Information

Deutsche Forschungsgemeinschaft (DFG)

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Learning environmental field exploration with computationally constrained underwater robots : Gaussian processes meet stochastic optimal control